Résumé: This study evaluates the performance of various linear and nonlinear time series models for simulating monthly precipitation at 12 stations in Algeria. We compare hybrid ARMA–ARCH/GARCH models (CARMA-ARCH, CARMA-GARCH), their optimally tuned versions, and Bayesian-optimized variants, as well as two bilinear hybrid models (BL-ARCH and BL-GARCH). This is the first work to integrate and compare this specific suite of models for precipitation forecasting. Two scenarios were considered: (1) a ‘uniform’ scenario using the same inputs (temperature at lags 0 and 1) for all stations, and (2) a ‘station-specific’ scenario allowing additional lagged inputs (e.g. temperature lag 6, precipitation lags) chosen per station. The results showed that in the first scenario, where model inputs were constant across all stations (temperature with lags 0 and 1), the simple hybrid models and their optimal versions performed well in precipitation simulation, yielding an average error of 17 mm. Under the fixed scenario (Scenario 1), the Bilinear-based models exhibited the weakest performance. In the second scenario, where both the number of inputs increased (including precipitation with various lags in addition to temperature and its lags) and input variability differed across stations, the rainfall simulation results for the studied stations showed that the average error decreased by 12 mm compared to the first scenario. The findings revealed that in the second scenario, With the addition of temperature at lags 3 and 6 and precipitation at lag 6, the top-performing models shifted toward BL-based models. Although the performance of all examined models was acceptable, BL-based models demonstrated superior performance compared to others due to their ability to better handle increased fluctuations caused by precipitation lags. Additionally, the results indicated that Bayesian optimization did not improve the performance of the base models despite increasing their complexity. Our findings provide a framework for selecting model complexity based on data availability, which can enhance forecasting accuracy.

Résumé: Reliable multi-station precipitation forecasting is challenging due to nonstationarity, noise, and spatial heterogeneity. This paper introduces a hybrid signal decomposition-machine learning benchmarking framework that integrates four decomposition methods (TQWT, MODWT, EWT, VMD) with three learners (Bagging, LSBoost, KNN), yielding twelve hybrid models. These models were rigorously tested across twelve stations in the Kébir Rhumel Basin using eight statistical metrics and distributional diagnostics to assess accuracy, stability, and generalization. Two dominant families emerged: TQWT-based hybrids achieved localized accuracy at four stations, while MODWT-Bagging led at eight stations and delivered the most consistent cross-station performance. MODWT-Bagging achieved R 2= 0.984–0.993 and NSE= 0.981–0.993, with RMSE ranging from 2.64 to 6.34, demonstrating strong predictive skill under …

Résumé: Seepage through earth dams should be carefully considered in the design and construction processes of such dams due to its effect on dam safety. In this study, The soft computing techniques, namely, Random Forest (RF), Multi-layer Perceptron (MLP), Random Subspace (RS) and M5P has been adopted to predicate the seepage through zoned earth dams. Al- Kramis earthen dam which located in Algeria was taken as case study. The data of thirteen years spened between (01/2006 to 12/2018) was collected and used. Different effective parameters were employed as input parameters to develop the soft computing models, including the reservoir water level (WL), the upstream right piezometer (h1), the upstream left piezometer (h2), the downstream right piezometer (h3), the downstream left piezometer (h4), while the observed seepage flows (Q) were used as output parameter. The performance of models was …

Résumé: Three machine learning models− Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR)− were applied to estimate monthly evaporation at Boukourdane Dam, Algeria. The dataset included 240 observations over 20 years, with the following inputs: max./min. air temperature, relative humidity, wind speed, and water temperature; the output being: evaporation.

Résumé: This study evaluates the performance of various linear and nonlinear time series models for simulating monthly precipitation at 12 stations in Algeria. We compare hybrid ARMA–ARCH/GARCH models (CARMA-ARCH, CARMA-GARCH), their optimally tuned versions, and Bayesian-optimized variants, as well as two bilinear hybrid models (BL-ARCH and BL-GARCH). This is the first work to integrate and compare this specific suite of models for precipitation forecasting. Two scenarios were considered: (1) a ‘uniform’ scenario using the same inputs (temperature at lags 0 and 1) for all stations, and (2) a ‘station-specific’ scenario allowing additional lagged inputs (e.g. temperature lag 6, precipitation lags) chosen per station. The results showed that in the first scenario, where model inputs were constant across all stations (temperature with lags 0 and 1), the simple hybrid models and their optimal versions performed well in precipitation simulation, yielding an average error of 17 mm. Under the fixed scenario (Scenario 1), the Bilinear-based models exhibited the weakest performance. In the second scenario, where both the number of inputs increased (including precipitation with various lags in addition to temperature and its lags) and input variability differed across stations, the rainfall simulation results for the studied stations showed that the average error decreased by 12 mm compared to the first scenario. The findings revealed that in the second scenario, With the addition of temperature at lags 3 and 6 and precipitation at lag 6, the top-performing models shifted toward BL-based models. Although the performance of all examined models was acceptable, BL-based models demonstrated superior performance compared to others due to their ability to better handle increased fluctuations caused by precipitation lags. Additionally, the results indicated that Bayesian optimization did not improve the performance of the base models despite increasing their complexity. Our findings provide a framework for selecting model complexity based on data availability, which can enhance forecasting accuracy.

Résumé: Accurately estimating evaporation in reservoir systems is an essential step in creating a water budget, as this information is crucial for the effective management of water resources, particularly in countries experiencing water stress. This investigation aims to test the success of tree-based (random forest, gradient boosting, extreme gradient boosting, adaptive boosting, and M5 prime) and neural network-based (multi-layer perceptron (MLP), Kolmogorov Arnold network (KAN), recurrent neural network, long short-term memory, and gated recurrent unit) methods, to estimation monthly evaporation at very important reservoir called Boukourdane Dam, which is located in a Mediterranean area in Algerian north. The KAN method was used for the first time in evaporation prediction. Data on minimum and maximum temperatures (Tmax, °C, Tmin, °C), wind speed (U, km/h), and relative humidity (H, %) between 1996 and 2016 were used as inputs to the models. Using lag values of the input data significantly increased the accuracy of the models. Although the applied machine learning models generally gave higher accuracy in predicting evaporation, neural network-based methods gave better results than tree-based ones. Although neural network-based methods give close results to each other, the MLP is the method that produces the best results for the test set. The most significant advantage of the KAN method, which consistently produces satisfactory results, is that it provides a clear and straightforward equation. Explainable artificial intelligence graphs showed that Tmax is the most effective parameter in evaporation estimation. The study results will provide convenience to decision-makers for efficient dam operation.